The experimental results show that SRA3 performs similarly to SRA and better than Two\(\_\) Arch2 on the DTLZ problems, especially on the DTLZ3 problem, where Two\(\_\) Arch2 has difficulty converging on the 15-objective DTLZ3 problem. But SRA3 performs poorly on WFG, a problem set where the range of each objective is different. And it is pointed out in [1]} that when the objectives are incomparable, usually the larger the range of the objective, the larger the impact of that objective on the indicator value, contrary to the fact that we want all objectives to contribute equally to the indicator. Meanwhile, [2]} points out that the value of indicator \(I_{\epsilon +}(A,B)\)  fluctuates a lot in different problems, which causes great trouble in determining the parameter \(k\)  of \(I_{\epsilon +}\)  indicator. Based on this, we tried to normalize SRA3, hoping to eliminate the bias caused by the different value ranges of different objectives and to keep the \(I_{\epsilon +}\)  indicator in a fixed range.
